Energy-Saving Electrochemical Hydrogen Production Coupled with Biomass-Derived Isobutanol Upgrading.

The widespread application of electrochemical hydrogen production faces significant challenges, primarily attributed to the high overpotential of the oxygen evolution reaction (OER) in conventional water electrolysis. To address this issue, an effective strategy involves substituting OER with the value-added oxidation of biomass feedstock, reducing the energy requirements for electrochemical hydrogen production while simultaneously upgrading the biomass. Herein, we introduce an electrocatalytic approach for the value-added oxidation of isobutanol, a high energy density bio-fuel, coupled with hydrogen production. This approach offers a sustainable route to produce the valuable fine chemical isobutyric acid under mild condition. The electrodeposited Ni(OH)2 electrocatalyst exhibits exceptional electrocatalytic activity and durability for the electro-oxidation of isobutanol, achieving an impressive faradaic efficiency of up to 92.4 % for isobutyric acid at 1.45 V vs. RHE. Mechanistic insights reveal that side reactions predominantly stem from the oxidative C-C cleavage of isobutyraldehyde intermediate, forming by-products including formic acid and acetone. Furthermore, we demonstrate the electro-oxidation of isobutanol coupled with hydrogen production in a two-electrode undivided cell, notably reducing the electrolysis voltage by approximately 180 mV at 40 mA cm-2 . Overall, this work represents a significant step towards improving the cost-effectiveness of hydrogen production and advancing the conversion of bio-fuels.

A Novel Model of Thyroid Nodule Segmentation for Ultrasound Images.

Ultrasonography is regarded as an effective technique for the detection, diagnosis and monitoring of thyroid nodules. Segmentation of thyroid nodules on ultrasound images is important in clinical practice. However, because in ultrasound images there is an unclear boundary between thyroid nodules and surrounding tissues, the accuracy of segmentation remains a challenge. Although the deep learning model provides an accurate and convenient method for thyroid nodule segmentation, it is unsatisfactory of the existing model in segmenting the margin of thyroid nodules. In this study, we developed boundary attention transformer net (BTNet), a novel segmentation network with a boundary attention mechanism combining the advantages of a convolutional neural network and transformer, which could fuse the features of both long and short ranges. Boundary attention is improved to focus on learning the boundary information, and this module enhances the segmentation ability of the network boundary. For features of different scales, we also incorporate a deep supervision mechanism to blend the outputs of different levels to enhance the segmentation effect. As the BTNet model incorporates the long range-short range connectivity effect and the boundary-regional cooperation capability, our model has excellent segmentation performance in thyroid nodule segmentation. The development of BTNet was based on the data set from Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital and the public data set. BTNet achieved good performance in the segmentation of thyroid nodules with an intersection-over-union of 0.810 and Dice coefficient of 0.892 Moreover, our work revealed great improvement in the boundary metrics; for example, the boundary distance was 7.308, the boundary overlap 0.201 and the boundary Dice 0.194, all with p values <0.05.

MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification.

The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.